23rd International Conference on Multiple Criteria Decision Making MCDM 2015 - Bridging Disciplines
August 2nd–7th, 2015, Hamburg, Germany
|01.10.2015|
The submission deadline for the Special Issue of the Computers and Operations Research Journal on "Evolutionary Multiobjective Optimization" has been extended to October 15, 2015.
|24.08.2015|
Now the Book of Abstracts can be downloaded. Also a short program is available. Note that each document will be provided in a printed version at the conference.
|13.08.2015|
The photos of the scientific and the social program can be downloaded here.
|31.07.2015|
The Master Track Schedule (also provided in a printed version) and the Session Chair Instructions can be downloaded.
|09.07.2015|
The public transportation ticket will be sent to registered participants who have pre-paid the conference fee by 27th of July at the latest.
|08.07.2015|
The final timetable is online.
|04/2015|
Conference registration opens.
|04/2015|
Apply for free voucher codes for traveling to Hamburg with FlixBus.
|03/2015|
Update on the social program.
IS-CPL-MCDA: Constructive Preference Learning in MCDA
Multiple Criteria Decision Aiding (MCDA) requires active participation of the stakeholders, which is often organized in an interactive process. In this process, phases of preference elicitation are interleaved with phases of computation of a recommended decision. A majority of recently developed MCDA methods, require the Decision Maker (DM) to provide preference information in an incomplete and indirect way, e.g., in the form of decision examples. Methods based on such preference information are considered more user-friendly than approaches based on explicitly provided parameter values, because they require less cognitive effort from the DM at the stage of preference elicitation. Moreover, since these methods assess instances of the preference model which are compatible with the provided incomplete/indirect preference information, the DM can see what are the consequences of his imprecise statements on the recommendation and easily investigate what are the relations between the provided preferences and the delivered results.
The session is devoted to multiple criteria techniques dealing with parameter uncertainty or incomplete, uncertain and indirect preference information. Ordinal regression and simulation-based methods are most welcome. Both theoretical and applied research will be presented in this session.